Regression analysis in data streams presents unique challenges due to the continuous, potentially infinite nature of the data and the phenomenon of concept drift, where the underlying data distribution or the relationship between variables changes over time. Traditional static regression models are ill-equipped to handle such dynamic environments. Adaptive linear filtering techniques offer a powerful paradigm for regression in data streams, allowing models to evolve and adjust to changing patterns. This article explores the application of linear adaptive filtering methods for regression tasks in data stream settings. We discuss the fundamental principles of adaptive filtering, common algorithms like Recursive Least Squares (RLS) and its variants, and their suitability for handling concept drift. By reviewing relevant literature on data stream mining, adaptive learning, and regression techniques, we highlight the advantages of using adaptive linear models, including their computational efficiency, ability to track changing relationships, and theoretical foundations in signal processing. While acknowledging limitations such as sensitivity to parameter choices and potential issues with non-linear relationships, this article argues that linear adaptive filtering provides a robust and efficient foundation for performing regression in dynamic data stream environments, serving as a crucial component in more complex adaptive learning systems.

Heart disease remains a leading cause of morbidity and mortality globally, necessitating accurate and early diagnostic tools to improve patient outcomes. The escalating volume of healthcare data, coupled with advancements in computational capabilities, has positioned machine learning (ML) as a transformative approach for enhancing the classification of cardiac conditions. This article provides a comprehensive evaluation of machine learning models, particularly focusing on Multilayer Perceptron (MLP) and Support Vector Machine (SVM) architectures, for their efficacy in classifying heart disease. We delve into the methodologies employed, including feature selection and model training, and analyze their performance metrics. The discussion highlights how these advanced computational techniques contribute to more precise, efficient, and reliable diagnostic support systems, thereby aiding clinicians in early detection and personalized treatment strategies.

Wireless Sensor Networks (WSNs) have emerged as a pivotal technology for diverse applications, enabling ubiquitous data collection and environmental monitoring. Comprising spatially distributed autonomous devices, WSNs present unique challenges in terms of their architectural design, power management, and inherent security vulnerabilities. This review synthesizes extant literature to explore the fundamental system architectures, critical design considerations for extending network lifetime, and essential security protocols integral to robust WSN deployment and operation. By examining established research, this article aims to provide a comprehensive understanding of the foundational principles and ongoing challenges in engineering reliable and secure wireless sensor systems, informing future research and practical implementations in various domains from environmental monitoring to emergency response.

Preeclampsia remains a leading cause of maternal and perinatal morbidity and mortality globally. Accurate and early prediction is crucial for timely intervention and improved outcomes. Machine learning (ML) has emerged as a promising approach for identifying individuals at high risk of developing preeclampsia by leveraging complex patterns within diverse datasets. However, translating promising ML research models into effective, reliable, and scalable clinical deployment presents significant challenges. This article reviews the current landscape of machine learning applications in preeclampsia prediction, focusing on identified deployment patterns and key predictive features. We synthesize findings from recent literature, discussing commonly employed ML algorithms, the types of data and features utilized (including maternal characteristics, biomarkers, and clinical history), and the reported predictive performance. Crucially, we examine the challenges and considerations related to the practical implementation of these models within healthcare systems, including data quality, model interpretability, integration into clinical workflows, and the necessity of robust MLOps practices. This review highlights the critical need to address deployment-related aspects to ensure that ML models for preeclampsia prediction can move beyond research settings and achieve real-world clinical impact, ultimately contributing to improved maternal health outcomes.

The proliferation of the Internet of Things (IoT) presents significant opportunities for enhancing the efficiency and control of residential electrical systems. This article explores the integration of IoT technology for comprehensive power monitoring and control in homes, aiming to reduce energy waste, improve fault detection, and enable remote management. Traditional home electrical systems, while foundational, often lack the granularity and real-time feedback necessary for optimal energy utilization. By leveraging IoT devices, such as sensors and smart controllers, homeowners can gain unprecedented insights into their power consumption patterns and exercise dynamic control over connected appliances and circuits. This paper discusses the architectural considerations, key components, and potential benefits of deploying an IoT-enabled power management system, highlighting its role in fostering energy sustainability and smart living.